A Framework for Data-Driven Process Optimization: The Process Data Quality Maturity Model

The successful implementation of data-driven process technologies requires not only a mature digital infrastructure but also high-quality process data. This paper introduces the Process Data Quality Maturity Model (PDQ MM), a comprehensive framework designed to assess and enhance process data quality. The PDQ MM focuses on the quantification of seven core dimensions - Completeness, Correctness, Credibility, Consistency, Availability, Timeliness, and Interoperability - alongside the four supporting dimensions Governance, Strategy, Technology, and Culture. By integrating quantitative and qualitative measures, the model addresses limitations in existing maturity frameworks, which often overlook process-specific data quality. A financial process case study illustrates the model's application, demonstrating strengths and weaknesses of not only the selected case but also the PDQ MM. Conducting a comparative analysis with another Digital Maturity Model highlights the PDQ MM's unique value in providing actionable, data-driven insights. Despite its strengths, the model's process-specific focus, resource intensity, and reliance on subjective inputs pose challenges, which future research should address through automation and scalability improvements. Overall, the PDQ MM offers organizations a robust tool for assessing digital maturity, optimizing processes, and unlocking the full potential of analytical techniques such as process mining, paving the way for more effective decision-making and sustained organizational growth.